# Existing Adversarial Defense and Image Sanitization Libraries

> Research date: 2026-03-25
> Purpose: Competitive landscape analysis for pixmask

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## 1. IBM Adversarial Robustness Toolbox (ART)

**Repository**: https://github.com/Trusted-AI/adversarial-robustness-toolbox
**PyPI**: `adversarial-robustness-toolbox` (latest: 1.20.1, released 2025-07-07)
**Governance**: Donated to Linux Foundation AI & Data (LFAI)

### Implementation

- Pure Python (99.8% of codebase)
- No compiled extensions or C++/Rust core
- Framework wrappers around NumPy, PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, CatBoost, GPy

### Install Size

- Source distribution: 2.9 MB
- Wheel: 1.1 MB
- Core install is lightweight; GPU/framework extras pulled separately via `[pytorch-image]`, `[tensorflow-image]`, `[all]`, etc.
- Python 3.10–3.12 supported

### Preprocessing Defenses (Complete List)

| Class | Method | Notes |
|---|---|---|
| `FeatureSqueezing` | Bit-depth quantization | Configurable depth |
| `SpatialSmoothing` | Local averaging / median filter | PyTorch + TF variants |
| `JpegCompression` | Lossy DCT quantization | Quality parameter |
| `TotalVarMin` | ROF / L-BFGS-B / CG optimization | Slow: iterative solver |
| `GaussianAugmentation` | Additive noise at training | Training-time only |
| `ThermometerEncoding` | Pixel thermometer binarization | Discretization levels |
| `PixelDefend` | PixelCNN manifold projection | Requires pretrained PixelCNN |
| `InverseGAN` | Latent optimization via GAN encoder | Requires TF session + trained model |
| `DefenseGAN` | GAN-based reconstruction | Requires TF session + trained model |
| `CutMix` / `Cutout` / `Mixup` | Data augmentation | Training-time only |
| `LabelSmoothing` | Soft targets | Training-time only |
| `VideoCompression` | H.264 via ffmpeg | Video modality |
| `Mp3Compression` | MP3 via librosa/soundfile | Audio modality |
| `Resample` | Windowed sinc audio resampling | Audio modality |

### Performance Characteristics

- No latency benchmarks published in documentation
- GAN-based defenses (InverseGAN, DefenseGAN) are computationally expensive — require iterative GAN forward passes
- TotalVarMin is an iterative optimization — not suitable for real-time inference without warm-start or fixed iteration caps
- Python GIL limits parallelism; no SIMD or vectorized C paths
- Framework dispatches add overhead for small batches (typical in API gateways)

### VLM / Multimodal Support

- No native VLM support. ART was designed for classifiers (CNNs, tabular models), not generative vision-language models
- No integration with HuggingFace `transformers` VLM pipelines (LLaVA, InternVL, Qwen-VL, GPT-4V)
- Preprocessing wrappers assume fixed-size image tensors, not the variable-resolution / patched inputs used by ViT-based VLM image encoders
- No defense against prompt injection embedded in images (adversarial text overlays, steganographic instructions)

### Verdict

ART is the most complete open-source toolkit for *research* evaluation of preprocessing defenses. It is not designed for production inference pipelines. Its Python-only core, lack of VLM integration, iterative solvers, and GAN-based defenses make it unsuitable as a drop-in preprocessing layer for high-throughput APIs.

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## 2. Foolbox

**Repository**: https://github.com/bethgelab/foolbox
**PyPI**: `foolbox` (latest: 3.3.3)
**Built on**: EagerPy (framework-agnostic tensor abstraction)

### What It Is

Foolbox is an **attack-only** library. It contains no defenses and no preprocessing utilities.

> "Foolbox only contains attacks, but no defenses and evaluation metrics." — official documentation

The library's `preprocessing=` parameter in model construction is a convenience for normalizing inputs before attack generation, not a defense mechanism.

### Supported Frameworks

PyTorch, TensorFlow, JAX — via EagerPy abstraction.

### Defense Gap

Zero. Foolbox is irrelevant to the defense landscape. It is the tool adversaries use, not defenders.

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## 3. MadryLab Robustness Library

**Repository**: https://github.com/MadryLab/robustness
**PyPI**: `robustness`
**Last release**: 1.2.1.post2 — **December 2020** (effectively unmaintained)

### What It Provides

- CLI for adversarially training classifiers (CIFAR-10, ImageNet)
- PGD-based attack generation for adversarial training data
- Pretrained robust models (L2, L-inf norms)
- Input space manipulation: adversarial example generation, representation inversion, feature visualization

### Critical Limitations

- **Training-time defense only.** No inference-time preprocessing transforms
- **Classifier-only.** No VLM, generative model, or multimodal support
- **Abandoned.** No commits since December 2020; Python 3.9+ compatibility uncertain
- **ImageNet/CIFAR-10 only.** Dataset assumptions baked into the codebase
- No pip-installable preprocessing pipeline usable by external models

### Verdict

Research artifact from the foundational adversarial training era. Not a competitor for inference-time image sanitization.

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## 4. AdvSecureNet

**Repository**: https://github.com/melihcatal/advsecurenet
**PyPI**: `advsecurenet`
**Paper**: arXiv:2409.02629 (University of Zurich, 2024)

### What It Provides

- PyTorch-based adversarial ML toolkit with **native multi-GPU support**
- 8 adversarial attack implementations (gradient-based, decision-based, white/black-box)
- 2 defenses: **adversarial training** and **ensemble adversarial training** — both training-time only
- Evaluation utilities for robustness benchmarking

### Preprocessing Defenses

**None.** AdvSecureNet explicitly focuses on training-time defenses. No inference-time preprocessing transforms are implemented.

### VLM Support

None. Computer vision classifiers only (PyTorch vision models). No generative model support.

### Verdict

A more ergonomic, GPU-friendly alternative to ART for adversarial training research. No relevance to inference-time image sanitization.

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## 5. PatchCleanser

**Repository**: https://github.com/inspire-group/PatchCleanser
**Paper**: USENIX Security 2022
**Install**: Manual clone + dependencies, no pip package

### What It Is

Certifiably robust defense against **adversarial patches** (localized, visible perturbations). Uses two rounds of pixel masking to neutralize any patch within a bounded region.

### Performance

- 83.9% top-1 clean accuracy on ImageNet (1000 classes)
- 62.1% top-1 certified robust accuracy against 2%-pixel square patch
- Requires running the classifier on O(N²) masked variants per image — quadratic inference overhead

### Limitations

- Patch-threat model only. Does not defend against global L-inf / L2 perturbations
- Not packaged for pip install; no production API
- No VLM support
- Quadratic inference cost prohibitive at API throughput

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## 6. DiffPure

**Repository**: https://github.com/NVlabs/DiffPure (NVIDIA Research)
**Paper**: ICML 2022
**Install**: Docker-only, no pip package

### What It Is

Adversarial purification via diffusion models. Adds forward-process noise to an adversarial image then denoises via reverse diffusion process, projecting back to the clean data manifold.

### Performance

- State-of-the-art on CIFAR-10, ImageNet, CelebA-HQ against L-inf, L2, and patch attacks
- Defense is model-agnostic (plugs in front of any classifier)

### Limitations

- **Massive compute cost.** Each inference requires a full diffusion denoising trajectory (hundreds of neural network forward passes)
- Not real-time: impractical for API endpoints; designed for offline/batch evaluation
- Docker-only install, no pip package, not maintained as a deployable library
- No VLM integration

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## 7. VLM-Specific Defense Tools (Research Only)

No production-ready, pip-installable defense tool specifically targets VLMs from adversarial image attacks. All existing work is academic:

### JailGuard (Microsoft Research)

- Detects jailbreak attempts by mutating inputs and measuring response discrepancy
- Operates on both text and image modalities
- Model-agnostic, training-free
- **Not a preprocessing sanitizer.** It's a detection layer that requires two forward passes through the VLM per query, doubling inference cost
- No pip package; research code only

### VALD (Multi-Stage Vision Attack Detection)

- Two-stage: image consistency check under transformations, then text-embedding divergence check
- Training-free
- Requires LLM invocation for final arbitration — high latency
- Research paper only (arXiv:2602.19570)

### VLMGuard

- Trains on unlabeled data to detect malicious prompts
- Requires fine-tuning a guard model per deployment
- Research code only

### ASTRA

- Computes steering vectors from image attribution, applies activation steering at inference
- Requires access to model internals (activations)
- Incompatible with closed-source VLM APIs (GPT-4V, Claude, Gemini)

### SmoothVLM

- Smoothing-based defense tailored for patch attacks on VLMs
- Research code only

### Tensor Decomposition Defense (2025)

- Decomposes and reconstructs vision encoder representations to filter adversarial noise
- Lightweight, training-free, applicable to any pretrained VLM
- No pip package

**Common pattern across all VLM defenses**: research repositories with paper-specific code, no packaging, no maintained API, no benchmarks on throughput or latency in production settings.

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## 8. Commercial / SaaS Solutions

### Mindgard (Lancaster University spinout)

- AI red teaming and DAST-AI (Dynamic Application Security Testing for AI)
- Tests adversarial robustness via attack simulation (evasion, extraction, poisoning)
- Offers CI/CD integration (GitHub Actions, CLI)
- **Focus: testing and evaluation, not runtime defense.** It finds vulnerabilities but does not sanitize inputs in production
- Neural-network agnostic: covers CV, LLM, NLP, audio, multimodal
- Custom enterprise pricing; not a preprocessing API

### Lakera Guard

- LLM prompt injection detection (text-focused)
- Self-hosted or SaaS cloud offering
- "Defense against multi-modal threats in audio and images is coming soon" — **not yet available** as of 2026
- Does not sanitize images; detects text-based injections

### LLM Guard (ProtectAI)

- Text-only input/output scanner: PII anonymization, prompt injection detection, topic restriction
- No image modality support
- Open source core with commercial support tier

### Rebuff

- Multi-layer prompt injection detection: heuristics + LLM scoring + canary tokens
- Text modality only; no image processing

### Cloud Provider Offerings (AWS, GCP, Azure)

- Content moderation APIs (AWS Rekognition, Google Cloud Vision SafeSearch, Azure Content Moderator): classify images as harmful/safe for CSAM, violence, adult content
- **None address adversarial perturbations.** These services are blind to imperceptible L-inf pixel attacks that cause model misclassification
- No adversarial preprocessing or sanitization primitives

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## 9. Gap Analysis

### What Every Existing Tool Fails to Provide

| Requirement | ART | Foolbox | MadryLab | AdvSecureNet | Commercial |
|---|---|---|---|---|---|
| Inference-time preprocessing (not training) | Partial | No | No | No | No |
| VLM / generative model integration | No | No | No | No | No |
| Closed-source VLM API compatibility (GPT-4V, Claude) | No | No | No | No | No |
| Sub-millisecond per-image overhead at API throughput | No | N/A | N/A | N/A | N/A |
| C++ / Rust native core for performance | No | No | No | No | No |
| Pip-installable, maintained, versioned | Partial | Yes | Dead | Yes | SaaS |
| Composable pipeline (chain multiple defenses) | No | N/A | N/A | No | No |
| Defense against adversarial text injected in images | No | No | No | No | No |
| Defense against scaling attacks | No | No | No | No | No |
| Benchmarked throughput / latency | No | N/A | N/A | No | No |

### Detailed Gaps

**1. No VLM-native preprocessing library exists.**
All existing tools (ART, AdvSecureNet) were designed for fixed-architecture classifiers with fixed-size inputs. VLMs use dynamic-resolution image encoding (ViT with variable patch counts), multi-image inputs, interleaved image-text, and often consume images via base64 or URL — none of which existing tools accommodate.

**2. No tool supports closed-source API-wrapped VLMs.**
ASTRA and VLMGuard require internal model activations or fine-tuning. JailGuard requires two VLM forward passes. Defenses that require model access are useless when the model is GPT-4V, Claude, or Gemini behind an API.

**3. No production-grade packaging with throughput SLAs.**
ART's TotalVarMin uses iterative optimization (L-BFGS-B). DefenseGAN/InverseGAN run GAN forward passes. DiffPure requires hundreds of diffusion steps. None are usable at the <5ms overhead required by real-time inference endpoints.

**4. No composable, configurable defense pipeline.**
All tools treat each defense as a standalone function. No library provides a declarative pipeline where defenses are chained, A/B tested, or selected by threat profile.

**5. No defense against the image-as-instruction attack class.**
Adversarial text embedded in images (adversarial typography, steganographic prompt injection) is not addressed by any existing tool. This is the dominant attack vector against deployed VLMs as of 2025–2026.

**6. No C++/Rust-accelerated image sanitization library.**
All Python implementations hit the GIL, have import overhead, and lack SIMD optimization. A C++ core with Python bindings (the pixmask architecture) does not exist in the open-source defense space.

**7. No maintained scaling-attack defense.**
Adversarial preprocessing / image-scaling attacks (Quiring et al., USENIX 2020) are unaddressed by any maintained pip-installable library.

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## 10. pixmask's Unique Value Proposition

Based on this landscape, pixmask occupies a gap that no existing tool fills:

1. **C++ core with Python bindings** — inference-time overhead measured in microseconds, not hundreds of milliseconds
2. **VLM-first design** — works as a preprocessing stage before any model, including closed-source API VLMs, without requiring model internals
3. **Composable pipeline** — chain bit-depth reduction, spatial smoothing, frequency filtering, and randomized transforms in one configurable pass
4. **Model-agnostic** — no retraining, no fine-tuning, no access to model weights required
5. **Scaling-attack defense** — addresses the adversarial preprocessing / image-scaling threat model that ART ignores
6. **Maintained and packaged** — pip-installable, versioned, with throughput benchmarks

The closest competitor (ART) requires 50–500ms for iterative defenses, has no VLM support, no C++ core, and was designed for research evaluation rather than production serving. The commercial tools (Mindgard, Lakera) do testing/detection, not sanitization.

**pixmask is the only project targeting fast, composable, VLM-compatible inference-time image sanitization with a compiled core.**

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## References

- ART documentation: https://adversarial-robustness-toolbox.readthedocs.io/
- ART GitHub: https://github.com/Trusted-AI/adversarial-robustness-toolbox
- ART PyPI: https://pypi.org/project/adversarial-robustness-toolbox/
- Foolbox GitHub: https://github.com/bethgelab/foolbox
- MadryLab robustness: https://github.com/MadryLab/robustness
- AdvSecureNet arXiv: https://arxiv.org/abs/2409.02629
- AdvSecureNet PyPI: https://pypi.org/project/advsecurenet/
- PatchCleanser (USENIX 2022): https://arxiv.org/abs/2108.09135
- DiffPure (ICML 2022): https://arxiv.org/abs/2205.07460
- VALD (arXiv 2025): https://arxiv.org/html/2602.19570
- JailGuard: https://arxiv.org/abs/2306.13213
- Mindgard: https://mindgard.ai/
- Lakera Guard: https://docs.lakera.ai/guard
- LLM Guard: https://protectai.com/llm-guard
